Object detection system

ABSTRACT

In a method involving the recognition of ambiguous objects from samples of 3D points, an initial constant is set for properties of prospective points of an object, and a sample of 3D points is received from a 3D sensor. When the sensed locations in space of points of objects represented by the 3D points are the same as those for the prospective points of an object, they are redefined as an actual points of objects, and the initial constants are replaced with corresponding properties represented by the 3D points. An ambiguous object is then recognized from a sparsely populated cluster of actual points of an object identified based on the replacement of the initial constants. The ambiguous object is confirmed based on signals representing its surface properties received from a polarization image sensor.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims benefit of U.S. Provisional Application No.62/244,780, filed on Oct. 22, 2015, which is herein incorporated byreference in its entirety.

TECHNICAL FIELD

The embodiments disclosed herein generally relate to autonomousoperation systems for vehicles, and object detection systems thereforethat are supported by LIDAR sensors.

BACKGROUND

Some vehicles include an operational mode in which a computing device isused to navigate and/or maneuver the vehicle along a travel route withminimal or no input from a human driver. Such vehicles include an objectdetection system that is configured to detect information about theenvironment surrounding the vehicle, including the presence of objectsin the environment. In these vehicles, the computing device isconfigured to process the detected information to determine how tonavigate and/or maneuver the vehicle through the environment. In manyinstances, the object detection system is supported by a LIDAR sensor.

SUMMARY

Disclosed herein are embodiments of methods involving the recognition ofambiguous objects from samples of 3D points. In one aspect, an initialconstant is set for at least one property of each of a plurality ofprospective points of an object having respective locations in space,and a sample of 3D points each representing a sensed location in spaceof a point of an object, and at least one sensed property of the pointof an object, is received from a 3D sensor. For each 3D point in thesample, when the sensed location in space of the point of an objectrepresented by the 3D point is the same as the location in space of aprospective point of an object, the prospective point of an object isredefined as an actual point of an object, and the initial constant setfor the at least one property of the actual point of an object isreplaced with the at least one sensed property of the point of an objectrepresented by the 3D point. An ambiguous object is then recognized froma sparsely populated cluster of actual points of an object identifiedbased on the replacement of the initial constant set for the at leastone property of the actual points of an object in the cluster with theleast one sensed property of the points of objects represented by the 3Dpoints. The ambiguous object is confirmed based on signals representingits surface properties received from a polarization image sensor.

In another aspect, an initial constant is set for at least one propertyof each of a plurality of prospective points of an object havingrespective locations in space, and a sample of 3D points eachrepresenting a sensed location in space of a point of an object, and atleast one sensed property of the point of an object, is received from a3D sensor. For each 3D point in the sample, when the sensed location inspace of the point of an object represented by the 3D point is the sameas the location in space of a prospective point of an object, theprospective point of an object is redefined as an actual point of anobject, and the initial constant set for the at least one property ofthe actual point of an object is replaced with the at least one sensedproperty of the point of an object represented by the 3D point. Anambiguous object is then recognized from a sparsely populated cluster ofactual points of an object identified based on the replacement of theinitial constant set for the at least one property of the actual pointsof an object in the cluster with the least one sensed property of thepoints of objects represented by the 3D points. A closest actual pointof an object in the cluster is identified, and the remaining actualpoints of an object in the cluster are assigned new simulated locationsin space that correspond to the location in space of the identifiedclosest actual point of an object in the cluster.

In yet another aspect, an initial constant is set for at least oneproperty of each of a plurality of prospective points of an objecthaving respective locations in space, and a sample of 3D points eachrepresenting a sensed location in space of a point of an object, and atleast one sensed property of the point of an object, is received from a3D sensor. The sample of 3D points is taken according to a sampling ratefor a set of samples of 3D points taken over a time period in which theposes of objects are projected to stay substantially the same. For each3D point in the sample, when the sensed location in space of the pointof an object represented by the 3D point is the same as the location inspace of a prospective point of an object, the prospective point of anobject is redefined as an actual point of an object, and the initialconstant set for the at least one property of the actual point of anobject is replaced with the at least one sensed property of the point ofan object represented by the 3D point. An ambiguous object is thenrecognized from a sparsely populated cluster of actual points of anobject identified based on the replacement of the initial constant setfor the at least one property of the actual points of an object in thecluster with the least one sensed property of the points of objectsrepresented by the 3D points. The sampling rate for the set of samplesof 3D points is increased based on the recognition of the ambiguousobject.

These and other aspects will be described in additional detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

The various features, advantages and other uses of the presentembodiments will become more apparent by referring to the followingdetailed description and drawing in which:

FIG. 1 is a schematic representation of a vehicle including an objectdetection system whose operation is supported by a LIDAR sensor and acomplementary polarization image sensor;

FIG. 2 is a flow diagram depicting the operations of a process forperforming object detection by recognizing objects from a set of samplesof output signals, observing and tracking objects from LIDAR data from atemporal series of sets of samples of output signals, confirming objectswith polarization image data, and simulating objects based on theresults for purposes of navigating and/or maneuvering the vehicle;

FIG. 3 is a graphical representation of the sampling of the LIDARsensor's output signals into a temporal series of sets of samples ofoutput signals;

FIG. 4 is a flow diagram depicting the operations of a process forrecognizing objects by receiving, binning and clustering LIDAR data; and

FIG. 5 is a pictorial representation of aspects of the operationsdepicted in FIG. 2.

DETAILED DESCRIPTION

This disclosure teaches an object detection system whose operation issupported by a 3D sensor, such as a LIDAR sensor. The object detectionsystem uses 3D points generated by the 3D sensor to recognize ambiguousobjects, for example, in an environment surrounding a vehicle. When itrecognizes ambiguous objects, the object detection system takes variousactions to support the vehicle in its efforts to appropriately avoid theambiguous objects when driving.

FIG. 1 shows a vehicle 10 including an autonomous operation system 12with an object detection system 20 whose operation is supported by aLIDAR sensor 22 and a complementary polarization image sensor 24. TheLIDAR sensor 22 and the polarization image sensor 24 are mounted on thevehicle 10 and positioned to have a common field of view in theenvironment surrounding the vehicle 10. Although the vehicle 10 isprovided as a non-limiting example of a mobile platform, it will beunderstood that the autonomous operation system 12 and its objectdetection system 20 could be implemented in other mobile platforms.Additionally, although the LIDAR sensor 22 is provided as a non-limitingexample of a 3D sensor, it will be understood that this description isapplicable in principle to other 3D sensors.

The LIDAR sensor 22 is configured to scan the environment surroundingthe vehicle 10 and generate signals, including but not limited to 3Dpoints, representing the objects in the environment surrounding thevehicle 10.

Generally, the LIDAR sensor 22 can include a transmitter and a receiver.The transmitter can be can component or group of components that cantransmit laser signals (e.g., laser light energy). As an example, thetransmitter can be a laser, laser rangefinder, LIDAR, and/or laserscanner. The laser signals can have any suitable characteristics. In oneor more arrangements, the laser signals can be from any suitable portionof the electromagnetic spectrum, such as from the ultraviolet, visible,or near infrared portions of the electromagnetic spectrum. The lasersignals can be eye safe.

The laser signals can be transmitted into the environment surroundingthe vehicle 10. The laser signals can impinge upon objects in theenvironment that are located in the path of the laser signals. The lasersignals may be transmitted in series of 360 degree spins around avertical (Z) axis of the vehicle 10, for example. Generally, when thelaser signals impinge upon an object, a portion of the laser signals canbe returned (e.g., by reflection) to the LIDAR sensor 22. The returnedportion of the laser signals can be captured at the LIDAR sensor 22 byits receiver, which may be, or include, one or more photodetectors,solid state photodetectors, photodiodes or photomultipliers, or anycombination of these.

Responsive to capturing the returned laser signals, the LIDAR sensor 22can be configured to output signals indicative of an object, or the lackthereof, in the environment surrounding the vehicle 10. The LIDAR sensor22 can include a global positioning system (GPS) or other positioningsystem for identifying its position, and an inertial measurement unit(IMU) for identifying its pose. According this configuration, the outputsignals may be 3D points representing objects in the environmentsurrounding the vehicle 10. The 3D points represent, among other things,the location in space of the points from which the returned lasersignals are received, and therefore, the location in space of points ofobjects on which the laser signals impinged. The LIDAR sensor 22 maydetermine the location in space of points of objects based on thedistance from the LIDAR sensor 22 to the points of objects, as well asthe position and pose of the LIDAR sensor 22 associated with thereturned laser signals. The distance to the points of objects may bedetermined from the returned laser signals using the time of flight(TOF) method, for instance. The output signals may also represent thelocations in space from which no returned laser signals are received,and therefore, the lack of points of objects in those locations in spaceon which the laser signals would otherwise have impinged.

The 3D points may further represent other aspects of the returned lasersignals, which, in turn, may represent other associated properties ofpoints of objects on which the incident laser signals impinged beyondtheir location in space. These aspects of the returned laser signals caninclude their amplitude, intensity or reflectivity, for instance, or anycombination of these.

The polarization image sensor 24 can also be configured to outputsignals indicative of an object, or the lack thereof, in the environmentsurrounding the vehicle 10. Generally, the polarization image sensor 24can include a polarizing filter positioned in front of an image sensor.The image sensor can be configured for capturing light or otherelectromagnetic energy from the environment surrounding the vehicle 10.Optionally, the environment can be illuminated by the transmitter of theLIDAR sensor 22. The image sensor can be, or include, one or morephotodetectors, solid state photodetectors, photodiodes orphotomultipliers, or any combination of these.

Responsive to capturing light or other electromagnetic energy, thepolarization image sensor 24 can be configured to output signalsindicative of objects, or the lack thereof, in the environmentsurrounding the vehicle 10. The signals can represent the polarizationsignatures and other surface properties of objects from which the lightor other electromagnetic energy is reflected.

In support the of the object detection system 20 as a whole, thepolarization image sensor 24 can complement the LIDAR sensor 22, forinstance, by generally compensating for its blind spots. The LIDARsensor 22 can have a blind spot for low reflective and unreflective(e.g., some dark or transparent) points of objects. Despite the physicalpresence of these points of objects at given locations in space, littleor none of the laser signals transmitted by the LIDAR sensor 22 andimpinging upon them are returned to the LIDAR sensor 22 for capture. Incases of low reflective points of objects, the 3D points output by theLIDAR sensor 22 may represent some but not all of their locations inspace as those from which returned lasers signals are received.Accordingly, there will be a sparse, or low, population of 3D pointsrepresenting the objects. In cases of unreflective points of objects,the 3D points output by the LIDAR sensor 22 may represent none of theirlocations in space as those from which returned lasers signals arereceived. Accordingly, there will be no population of 3D pointsrepresenting the objects.

The polarization image sensor 24 can compensate for these blind spots byoutputting signals representing the polarization signatures and othersurface properties of points of the objects, which in the case of thepolarization image sensor 24 are largely the product of their physicalpresence, as opposed to their reflectivity.

The vehicle 10 includes a computing device 30 to which the LIDAR sensor22 and the polarization image sensor 24 are communicatively connectedthrough one or more communication links 32. Although the computingdevice 30 and either or both of the LIDAR sensor 22 and the polarizationimage sensor 24 may be dedicated to the autonomous operation system 12and its object detection system 20, it is contemplated that some or allof these could also support the operation of other systems of thevehicle 10.

The computing device 30 may include a processor 40 communicativelycoupled with a memory 42. The processor 40 may include any devicecapable of executing machine-readable instructions, which may be storedon a non-transitory computer-readable medium, for example the memory 42.The processor 40 may include a controller, an integrated circuit, amicrochip, a computer, and/or any other computing device. The memory 42may include any type of computer readable medium suitable for storingdata and algorithms. For example, the memory 42 may include RAM, ROM, aflash memory, a hard drive, and/or any device capable of storingmachine-readable instructions.

The computing device 30 may also include an input/output interface 44for facilitating communication between the processor 40 and the LIDARsensor 22 and the polarization image sensor 24. Although the computingdevice 30 is schematically illustrated as including a single processor40 and a single memory 42, in practice the computing device 30 mayinclude a plurality of components, each having one or more memories 42and/or processors 40 that may be communicatively coupled with one ormore of the other components. The computing device 30 may be a separatestandalone unit or may be configured as a part of a central controlsystem for the vehicle 10.

The various algorithms and data for the autonomous operation system 12and its object detection system 20, as well as the other systems of thevehicle 10, may reside in whole or in part in the memory 42 of thecomputing device 30. In operation of the object detection system 20, thesignals output by the LIDAR sensor 22 are stored in the memory 42 asLIDAR data 50, and the signals output by the polarization image sensor24 are stored in the memory 42 as polarization image data 52. Asdescribed in additional detail below, the algorithms and data for theautonomous operation system 20 are included as part of a detectionmodule 60 and a planning module 62.

Although the various algorithms and data for the autonomous operationsystem 12 and its object detection system 20 are described withreference to the computing device 30 of the vehicle 10 for simplicity,it will be understood that these may reside in whole or in part in amemory of a computing device separate from the vehicle 10. In thesecases, the vehicle 10 may also include an integrated mobilecommunication system 70 with variously configured communication hardwarefor wirelessly transmitting data between the computing device 30 and amobile network, such as a cellular network. The mobile communicationsystem 70 and the mobile network together enable the computing device 30to wirelessly communicate with other devices connected to the mobilenetwork, such as a remote server that may similarly be, or include, acomputing device including one or more processors and one or morememories, or another vehicle that may similarly include an objectdetection system with a computing device including one or moreprocessors and one or more memories.

The mobile communication system 70 of the vehicle 10 may include anintegrated mobile network transceiver 72 configured to transmit andreceive data over the mobile network. The mobile network transceiver 72may be communicatively connected to the computing device 30 though amobile network transceiver communication link 74, with the input/outputinterface 44 facilitating communication between the processor 40 and thememory 42 and the mobile network transceiver 72. The mobile networktransceiver 72 includes a transmitter for wirelessly transferring datafrom the computing device 30 to the mobile network and a receiver forwirelessly transferring data from the mobile network to the computingdevice 30.

The operations of a process 200 by which the detection module 60 employsthe remainder of the object detection system 20 of the vehicle 10 toperform object detection are shown in FIG. 2.

Initially, in operation 202, the LIDAR sensor 22 is signaled to operate,and its output signals, including 3D points representing the location inspace of points of objects in the environment surrounding the vehicle10, if any, as well as other associated properties of those points ofobjects represented by aspects of returned laser signals, are receivedand stored as LIDAR data 50. Also, the polarization image sensor 24 issignaled to operate, and its output signals representing surfaceproperties such as polarization signatures of objects, if any, in theenvironment surrounding the vehicle 10 are received and stored aspolarization image data 52.

The LIDAR sensor 22 may operate at high frequencies, for instance, onthe order of 10 to 1,000 KHz, with each round of operation resulting ina sampling of output signals. In an implementation of the process 200represented in FIG. 3, at a given time T_(m), a set of n samples ofoutput signals T_(m)(1 . . . n) (i.e., T_(m)(1), T_(m)(2), T_(m)(3) . .. T_(m)(n)) can be taken for corresponding n rounds of operation of theLIDAR sensor 22. The set of n samples of output signals T_(m)(1 . . . n)is taken over a time period. With a high frequency of the sampling ofoutput signals, the time period may be short enough that the poses ofany objects in the environment surrounding the vehicle 10 are projectedto stay substantially the same as the set of n samples of output signalsT_(m)(1 . . . n) is taken, even if the objects are moving with respectto the LIDAR sensor 22 or otherwise with respect to the vehicle 10.

Moreover, a temporal series of sets of n samples of output signalsT_(m(x-1) . . . m)(1 . . . n) can be taken over multiple consecutivetimes. The temporal series of sets of n samples of output signalsT_(m(x-1) . . . m)(1 . . . n) may include the set of n samples of outputsignals T_(m)(1 . . . n) from the most current time T_(m), and similarsets of n samples of output signals from x previous times. The timeperiods between the most current time T_(m), and successive preceding xprevious times may be long enough that the poses of any objects in theenvironment surrounding the vehicle 10 change as the temporal series ofsets of n samples of output signals T_(m(x-1) . . . m)(1 . . . n) istaken if the objects are moving with respect to the LIDAR sensor 22 orotherwise with respect to the vehicle 10.

Optionally, in operation 204, parameters for output, receipt, storage orother predicates to further consideration of the LIDAR data 50 can beset, either in the LIDAR sensor 22 or in the computing device 30, as thecase may be, that prescribe minimums, maximums or ranges for anyproperties of interest of points of objects. For example, a parametercan be set that prescribes the maximum distance from the vehicle 10 ofpoints of objects for further consideration. In this example, in orderfor a 3D point to satisfy this parameter and qualify for furtherconsideration, the location in space of the point of an object that the3D point represents must be within the prescribed maximum distance fromthe vehicle 10.

In operation 206, objects are recognized from the set of n samples ofoutput signals T_(m)(1 . . . n) from the most current time T_(m),according to the operations of a process 400 shown in FIG. 4 anddescribed below. For the time T_(m), one, some or all of the operationsdescribed below may be repeated across the set of n samples of outputsignals T_(m)(1 . . . n). In other words, object recognition is based onthe set of n samples of output signals T_(m)(1 . . . n). As noted above,the time period over which the set of n samples of output signalsT_(m)(1 . . . n) is taken is short enough that the poses of any objectsin the environment surrounding the vehicle 10 stay substantially thesame. In previous iterations of operation 206 and the process 400, thesame and other objects may have been recognized from similar sets of nsamples of output signals from previous times in the temporal series ofsets of n samples of output signals T_(m(x-1) . . . m)(1 . . . n).

One or more 3D box cells are defined in operation 402. Each 3D box celloccupies a respective 3D space in the environment surrounding thevehicle 10, and has prospective points of objects in it. Beyond theprospective points of objects themselves, various properties of theprospective points of objects are also defined. These properties of theprospective points of objects correspond to those of points of objectsthat could be represented in 3D points output by the LIDAR sensor 22 inresponse to capturing returned laser signals impinging on them.Accordingly, the properties of the prospective points of objects mayinclude their location in space representing, for instance, theirdistance to the vehicle 10, as well as associated properties, such astheir amplitude, intensity or reflectivity, for instance, or anycombination of these.

Additionally, for each 3D box cell, initial constants are set for one,some or all of the properties of the prospective points of objects inthe 3D box cell. These initial constants may, for example, be maximum orminimum values for the properties of the prospective points of objectsin the 3D box cell. Accordingly, these initial constraints may be themaximum or minimum values for their location in space representing, forinstance, their distance to the vehicle 10, as well as for theiramplitude, intensity or reflectivity, for instance, or any combinationof these.

The points of objects in the environment surrounding the vehicle 10 arerepresented in the LIDAR data 50 by respective 3D points. The 3D pointsrepresent, among other things, the location in space of the points ofobjects. Based on their locations in space, the actual points of objectsare binned in the 3D box cells in operation 404. More specifically,based on their locations in space, as represented by 3D points, theactual points of objects are binned in the appropriate 3D box cells thatcontain the corresponding prospective points of objects with the samelocations in space.

As the points of objects are binned in the 3D box cells, the initialconstants set for the properties of the corresponding prospective pointsof objects in the 3D box cells are replaced with the sensed propertiesof the points of objects represented by the 3D points. For example, aninitial constant set for the location in space of a prospective point ofan object in a 3D box cell representing its distance to the vehicle 10may be replaced with the sensed location in space of a point of anobject represented by a 3D point representing its actual distance to thevehicle 10. Further, for example, an initial constant set for theamplitude, intensity and/or reflectivity of a prospective point of anobject in a 3D box cell may be replaced with the associated sensedamplitude, intensity and/or reflectivity, as the case may be, of a pointof an object represented by a 3D point.

In each 3D box cell, in operations 406 and 408, the resident points ofindividual objects are clustered, and the clustered points arerecognized as objects in the environment surrounding the vehicle 10. Theclustering may involve identifying updates from the initial constantsset for one, some or all of the properties, such as location in spacerepresenting distance to the vehicle 10, or amplitude, intensity and/orreflectivity, of prospective points of objects in the 3D box cell totheir respective replacements resulting from the binning of points ofobjects into the 3D box cell. For a given prospective point of an objectin the 3D box cell, in the case of an updated value from its initialconstant, a point of an object is identified and clustered with otheridentified points of objects, while in the case of an un-updated valuefrom its initial constant, a point of an object not identified. Theclustering may involve detecting a certain amount of deviation in one ormore properties of interest, such as location in space representingdistance to the vehicle 10, or amplitude, intensity or reflectivity of apoint of an object under consideration from those properties of interestof one or more neighboring points of objects.

As a component of the overall clustering, the recognized object may beidentified as an unambiguous object, in operation 406, or as anambiguous object, in operation 408.

As shown with additional reference to FIG. 5, often as a result of anobject in the environment surrounding the vehicle 10 being reflective,the object is well represented via a high population of 3D points and,as a result, a high population of resident points of the object in the3D box cells. Accordingly, there is a high confidence of the objectactually existing in the environment surrounding the vehicle 10. Theobjects recognized from these clustered points of objects may beidentified as unambiguous objects.

However, often as a result of an object in the environment surroundingthe vehicle 10 being low reflective, the object is represented, but notwell, via a low population of 3D points and, as a result, a lowpopulation of resident points of the object in the 3D box cells. Theresident points of the object in the 3D box cells may, for instance, belocalized around an edge of an associated group of prospective points ofobjects from which no points of objects are identified. In this andother examples of low populations of resident points of an object in the3D box cells, the resident points of the object in the 3D box cell maybe clustered with associated groups of prospective points of objectsfrom which no points of objects are identified, and the objectsrecognized from these clustered points may be identified as ambiguousobjects. On the other hand, the lack of a recognized object may beidentified from no population of resident points of objects in the 3Dbox cells.

As noted above, object recognition is based on the set of n samples ofoutput signals T_(m)(1 . . . n). The level of consistency of outputsignals for a recognized object across the set of n samples of outputsignals T_(m)(1 . . . n) can be identified, and the noise across the setof n samples of output signals T_(m)(1 . . . n) for a recognized objectcan be reduced, in operation 410. Optionally, to promote objectrecognition, the parameters for accumulating the set of n samples ofoutput signals T_(m)(1 . . . n) can be optimized in-process based on thelevel of consistency. For example, in cases of identified ambiguousobjects, the sampling rate, and therefore n, can be increased in orderto accumulate more samples of output signals as compared to cases ofidentified unambiguous objects.

In operation 208 of the process 200, once objects are recognized fromthe set of n samples of output signals T_(m)(1 . . . n) from the mostcurrent time T_(m), the temporal series of sets of n samples of outputsignals T_(m(x-1) . . . m)(1 . . . n) including the set of n samples ofoutput signals T_(m)(1 . . . n) from the most current time T_(m), andsimilar sets of n samples of output signals from x previous times, canbe evaluated to observe and track the recognized objects over time inorder to identify their continuity of location and motion.

In operation 210, surfaces in the environment surrounding the vehicle 10can be recognized based on their surface properties represented in thepolarization image data 52, and recognized objects can be confirmed byidentifying the recognized surfaces as corresponding to the recognizedobjects. Although this is particularly advantageous for those recognizedobjects identified as ambiguous objects, the robustness of the objectdetection system 20 is improved for both those recognized objectsidentified as ambiguous objects and those recognized objects identifiedas unambiguous objects.

Recognized objects identified as ambiguous objects may, for instance, bethe product of blooming or virtual reflections caused by the sun, forinstance, or by other illumination sources, optics or reflectors, suchas multipath or other types of reflectors, for instance in the headlightunits of other vehicles in the environment surrounding the vehicle 10.If a recognized object is an ambiguous object, it can be rejected if itcannot be confirmed that it corresponds to a recognized surface. Thisreduces or eliminates the influence of blooming, virtual reflections orother optical illusions, and reduces or eliminates the false positiveswhich would otherwise occur.

As shown in FIG. 5, for the points of a recognized object in the 3D boxcells, the closest point of the object to the vehicle 10 may beidentified from among the points of the object from their locations inspace. This may represent, for instance, the closest end of the object.In operation 212, for the remaining points of the object in the 3D boxcells, their locations in space may be reassigned the same distance tothe vehicle 10 as that for the identified closest point of the object tothe vehicle 10, with the result being LIDAR-based simulated points ofthe object.

Often as a result of an object in the environment surrounding thevehicle 10 being unreflective, the object is not represented via pointsof an object in the 3D box cells. These objects, however, will havesurfaces in the environment surrounding the vehicle 10 recognized basedon their surface properties represented in the polarization image data52. For any remaining recognized surfaces surrounding the recognizedobject but not identified as corresponding to the recognized object,additional polarization image-based points of the object may besimulated in the in the 3D box cells, while assigning, for theirlocation in space, the same distance to the vehicle 10 as that for theidentified closest point of the object to the vehicle 10.

In collaboration with the process 200, the planning module 62 may planhow to navigate, maneuver or otherwise drive the vehicle 10 through theenvironment surrounding the vehicle 10 based on the object representedby the LIDAR-based simulated points of the object and the polarizationimage-based simulated points of the object, while avoiding the object,and the autonomous operation system 12 of the vehicle 10 can drive thevehicle 10 along the route according to the plan.

While recited characteristics and conditions of the invention have beendescribed in connection with certain embodiments, it is to be understoodthat the invention is not to be limited to the disclosed embodimentsbut, on the contrary, is intended to cover various modifications andequivalent arrangements included within the spirit and scope of theappended claims, which scope is to be accorded the broadestinterpretation so as to encompass all such modifications and equivalentstructures as is permitted under the law.

What is claimed is:
 1. A method, comprising: setting, using a detectionmodule executable by at least one processor, an initial constant for atleast one property of each of a plurality of spatially predefinedprospective points of an object having respective locations in space andprospectively representable by 3D points from a 3D sensor; receiving,using the detection module executable by the at least one processor,from the 3D sensor, a sample of 3D points each representing a sensedlocation in space of a point of an object, and at least one sensedproperty of the point of an object; using the detection moduleexecutable by the at least one processor, for each 3D point in thesample, when the sensed location in space of the point of an objectrepresented by the 3D point is the same as the location in space of aprospective point of an object, redefining the prospective point of anobject as an actual point of an object, and replacing the initialconstant set for the at least one property of the actual point of anobject with the at least one sensed property of the point of an objectrepresented by the 3D point; clustering, using the detection moduleexecutable by the at least one processor, actual points of an objectwith associated un-redefined prospective points of an object based onthe replacement of the initial constant set for the at least oneproperty of the actual points of an object in the cluster with the atleast one sensed property of the points of objects represented by the 3Dpoints; recognizing, using the detection module executable by the atleast one processor, an ambiguous object from a sparse population of theactual points of an object in the cluster relative to the associatedun-redefined prospective points of an object in the cluster; andconfirming, using the detection module executable by the at least oneprocessor, the ambiguous object based on signals representing itspolarization signature received from a polarization image sensor.
 2. Themethod of claim 1, wherein the at least one property of each of theplurality of prospective points of an object, and the at least onesensed property of the point of an object, are each at least one of anamplitude, an intensity and a reflectivity.
 3. The method of claim 2,wherein the initial constant set for at the at least one property ofeach of the plurality of prospective points of an object is a maximum orminimum value of the at least one of an amplitude, an intensity and areflectivity.
 4. The method of claim 1, further comprising: driving,using a planning module executable by the at least one processor, avehicle along a route based on the recognized and confirmed ambiguousobject.
 5. The method of claim 1, further comprising: identifying, usingthe detection module executable by the at least one processor, a closestactual point of an object in the cluster; and assigning, using thedetection module executable by the at least one processor, the remainingactual points of an object in the cluster new simulated locations inspace that correspond in closeness to the location in space of theidentified closest actual point of an object in the cluster.
 6. Themethod of claim 5, further comprising: driving, using a planning moduleexecutable by the at least one processor, a vehicle along a route basedon the new simulated locations in space of the actual points of anobject in the cluster.
 7. The method of claim 1, wherein the 3D sensoris a LIDAR sensor.
 8. A method, comprising: setting, using a detectionmodule executable by at least one processor, an initial constant for atleast one property of each of a plurality of spatially predefinedprospective points of an object having respective locations in space andprospectively representable by 3D points from a 3D sensor; receiving,using the detection module executable by the at least one processor,from the 3D sensor, a sample of 3D points each representing a sensedlocation in space of a point of an object, and at least one sensedproperty of the point of an object; using the detection moduleexecutable by the at least one processor, for each 3D point in thesample, when the sensed location in space of the point of an objectrepresented by the 3D point is the same as the location in space of aprospective point of an object, redefining the prospective point of anobject as an actual point of an object, and replacing the initialconstant set for the at least one property of the actual point of anobject with the at least one sensed property of the point of an objectrepresented by the 3D point; clustering, using the detection moduleexecutable by the at least one processor, actual points of an objectwith associated un-redefined prospective points of an object based onthe replacement of the initial constant set for the at least oneproperty of the actual points of an object in the cluster with the atleast one sensed property of the points of objects represented by the 3Dpoints; recognizing, using the detection module executable by the atleast one processor, an ambiguous object from a sparse population of theactual points of an object in the cluster relative to the associatedun-redefined prospective points of an object in the cluster;identifying, using the detection module executable by the at least oneprocessor, a closest actual point of an object in the cluster; andassigning, using the detection module executable by the at least oneprocessor, the remaining actual points of an object in the cluster newsimulated locations in space that correspond in closeness to thelocation in space of the identified closest actual point of an object inthe cluster.
 9. The method of claim 8, wherein the at least one propertyof each of the plurality of prospective points of an object, and the atleast one sensed property of the point of an object, are each at leastone of an amplitude, an intensity and a reflectivity.
 10. The method ofclaim 9, wherein the initial constant set for at the at least oneproperty of each of the plurality of prospective points of an object isa maximum or minimum value of the at least one of an amplitude, anintensity and a reflectivity.
 11. The method of claim 8, furthercomprising: driving, using a planning module executable by the at leastone processor, a vehicle along a route based on the new simulatedlocations in space of the actual points of an object in the cluster. 12.The method of claim 8, further comprising: confirming, using thedetection module executable by the at least one processor, the ambiguousobject based on signals representing its polarization signature receivedfrom a polarization image sensor.
 13. The method of claim 8, wherein the3D sensor is a LIDAR sensor.
 14. A method, comprising: setting, using adetection module executable by at least one processor, an initialconstant for at least one property of each of a plurality of spatiallypredefined prospective points of an object having respective locationsin space and prospectively representable by 3D points from a 3D sensor;receiving, using the detection module executable by the at least oneprocessor, from the 3D sensor, a sample of 3D points taken according toa sampling rate for a set of samples of 3D points taken over a timeperiod in which the poses of objects are projected to stay substantiallythe same, the 3D points each representing a sensed location in space ofa point of an object, and at least one sensed property of the point ofan object; using the detection module executable by the at least oneprocessor, for each 3D point in the sample, when the sensed location inspace of the point of an object represented by the 3D point is the sameas the location in space of a prospective point of an object, redefiningthe prospective point of an object as an actual point of an object, andreplacing the initial constant set for the at least one property of theactual point of an object with the at least one sensed property of thepoint of an object represented by the 3D point; clustering, using thedetection module executable by the at least one processor, actual pointsof an object with associated un-redefined prospective points of anobject based on the replacement of the initial constant set for the atleast one property of the actual points of an object in the cluster withthe at least one sensed property of the points of objects represented bythe 3D points; recognizing, using the detection module executable by theat least one processor, an ambiguous object from a sparse population ofthe actual points of an object in the cluster relative to the associatedun-redefined prospective points of an object in the cluster; andincreasing, using the detection module executable by the at least oneprocessor, the sampling rate for the set of samples of 3D points basedon the recognition of the ambiguous object.
 15. The method of claim 14,wherein the at least one property of each of the plurality ofprospective points of an object, and the at least one sensed property ofthe point of an object, are each at least one of an amplitude, anintensity and a reflectivity.
 16. The method of claim 15, wherein theinitial constant set for at the at least one property of each of theplurality of prospective points of an object is a maximum or minimumvalue of the at least one of an amplitude, an intensity and areflectivity.
 17. The method of claim 14, further comprising: repeating,using the detection module executable by the at least one processor, thesetting, receiving, redefining, replacing and recognizing steps for theset of samples of 3D points; and identifying, using the detection moduleexecutable by the at least one processor, the consistency of andreducing the noise among the actual points of an object in the clusters.18. The method of claim 14, further comprising: repeating, using thedetection module executable by the at least one processor, the setting,receiving, redefining, replacing and recognizing steps to take atemporal series of sets of samples of 3D points taken over consecutivetimes over which the poses of objects are projected to change; andtracking, using the detection module executable by the at least oneprocessor, the ambiguous object based on its repeated recognition acrossthe temporal series of sets of samples of 3D points.
 19. The method ofclaim 18, further comprising: driving, using a planning moduleexecutable by the at least one processor, a vehicle along a route basedon the tracked ambiguous object.
 20. The method of claim 14, wherein the3D sensor is a LIDAR sensor.